The ability of an autonomous vehicle to perform 3D tracking is essential for safe planing and navigation in cluttered environments. The main challenges for multi-object tracking (MOT) in autonomous driving applications reside in the inherent uncertainties regarding the number of objects, when and where the objects may appear and disappear, and uncertainties regarding objects' states. Random finite set (RFS) based approaches can naturally model these uncertainties accurately and elegantly, and they have been widely used in radar-based tracking applications. In this work, we developed an RFS-based MOT framework for 3D LiDAR data. In partiuclar, we propose a Poisson multi-Bernoulli mixture (PMBM) filter to solve the amodal MOT problem for autonomous driving applications. To the best of our knowledge, this represents a first attempt for employing an RFS-based approach in conjunction with 3D LiDAR data for MOT applications with comprehensive validation using challenging datasets made available by industry leaders. The superior experimental results of our PMBM tracker on public Waymo and Argoverse datasets clearly illustrate that an RFS-based tracker outperforms many state-of-the-art deep learning-based and Kalman filter-based methods, and consequently, these results indicate a great potential for further exploration of RFS-based frameworks for 3D MOT applications.
翻译:自主飞行器进行3D跟踪的能力对于在混乱的环境中安全规划和导航至关重要。自主驾驶应用中多目标跟踪的主要挑战在于物体数量、时间和地点以及物体可能出现和消失的内在不确定性,以及物体状态的不确定性。基于随机限量集(RFS)的方法可以自然地准确和优雅地模拟这些不确定性,并在基于雷达的跟踪应用中广泛使用。在这项工作中,我们为3D LiDAR数据开发了一个基于RFS的MOT框架。在Partiuclar中,我们建议建立一个Poisson多Bernoulli混合物(PMBM)过滤器,以解决自动驾驶应用的现代MOT问题。据我们所知,这是首次尝试利用基于3D LDAR的数据为MOT应用程序使用基于3D的3DLDAR数据进行综合验证,同时利用行业领导人提供的具有挑战性的数据集加以验证。我们的PMBM追踪器在公共途径和Argovers数据集方面的优异实验结果。我们提议建立一个PMBMBM(PMB)过滤器过滤器过滤器过滤器过滤器(RFS-FS-CMAR-CFS-C-CFS-C-C-C-FS-C-C-C-C-FS-C-C-C-C-C-C-C-C-C-C-C-BAR-C-C-FS-C-C-C-C-FS-C-C-C-C-C-C-C-BAR-C-C-C-C-C-CFS-C-C-C-C-C-C-C-FS-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-FS-FS-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-